Center for Automated Learning and Discovery
School of Computer Science, Carnegie Mellon University
Learning from Labels and Unlabeled Data
Xiaojin Zhu, Zoubin Ghahramani
We investigate the use of unlabeled data to help labeled data in classification. We propose a simple iterative algorithm, label propagation, to propagate labels through the dataset along high density areas defined by unlabeled data. We give the analysis of the algorithm, show its solution, and its connection to several other algorithms. We also show how to learn parameters by minimum spanning tree heuristic and entropy minimization, and the algorithm's ability to do feature selection. Experiment results are promising.
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